{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "exact-volunteer",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "violent-geometry",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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"iVBORw0KGgoAAAANSUhEUgAAAN0AAADnCAYAAACaJVPRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAARZklEQVR4nO2d2XLiyhJFtwYkgbvP///mCdtMQtJ9OHdXJ+kSYLdJBu8VoQCDEMKhRWZlDRTTNEEIEUd56xMQ4qch6YQIRtIJEYykEyIYSSdEMPWZ51XaFOLrFLkHFemECEbSCRGMpBMiGEknRDCSTohgJJ0QwUg6IYKRdEIEI+mECEbSCRGMpBMiGEknRDCSTohgJJ0QwUg6IYKRdEIEI+mECEbSCRGMpBMiGEknRDCSTohgJJ0QwUg6IYKRdEIEI+mECEbSCRHMuWXVxTfwtz+8WRTZ1bnPHvvU68TtUKQTIhhFugcgF80UxR4XSfcJLkkTv1OGufcriiI999n3i/4M4iOS7pN89WL/2/cjFO673z/6c/1k1Kb7In9bHLmEW4ktrosi3Z1TlnHfi4pyMRRnvuX0FWjg/8qnd18t6Vtyx8illp857jmJTrUZxbeQ/UdKum8m9/+8Znvpq5VNiRVC9p+s9PJK2Kg498XG5/y+dn8vbFEUH+7bx/y+Fol2HyjSfYJL0z3eH8dxViIrGPfL7T+OY3ovbmVZZm+9kP4cTz0nroIiXQTTNGEYhg8SHQ6HI7nGccQwDBjHMT13OByOXs/XAf8JUlUVqqpCXdeoqgqLxSLdlmWZtqIoUNf1B7lYlOE+4jZIui/AQoqPfDZ6+SjW932SjcJx43N+n2masN/vAfwnCoVbLBao6xrjOKKqKkzTlIT08gF/ouQ4jtnzloCxSLpPkqtg+nTSisNts9mkiMao1vc9DocD9vs9hmHAfr8/EnIcR2y3WwA4Eq7rOtR1jdVqhbqu0XVdEtGK59t8Pv1UxLsNku5CbEHjklI7U8PD4YBhGI5uKZqXbrfbHcnIxwAk4ZqmwTiOWCwWKY20EZUp5H6/P4p6ZVmmlFOy3ZYfK50V57MXoI9wNnrYSOjlYjTr+x7b7Rb7/T7tczgcsNvt0Pc9drvdkYiUq2katG2LaZrQNA0AYLFYHJ1H0zQpnWU7kIJyH4rpP8dX/x/ic/xY6b7CXBXSXqQ2PaRowzBgu90eCbXdbpNk2+0Wh8MBm80G+/0+7cPHACThuq5D3/do2xbDMKTIR+q6/hDRhmFIXwjcJNbtkHRfwApnb3nft+tYLOn7Hvv9Pm2Ua7PZoO/7JJ2Ngu/v7wD+k67vewzDkIoojHJMPReLRap2+rTTVlI11vK2SLpvhBe2rUyyDcfIRrHW6zU2mw12ux3e39/R9z3e3t4+RMF///0X0zShbVssl0u8vLxgv99juVxiHEcsl0sAfyqUTENZ0bRR7XA4pEKLTzM1yyAOSYf8MK1cdwBv/cbHGdkY1bixOOKj3W63SxtTz91ulyIdox+Pz2LIfr9HXddJaKaxvrthHEeUZZkd9SK5boekO4NPIe3F7fvjGN1s2kjBGNXW63W6fX9/P4p0/NtGOqaXTBvLssRut0NVVUfy2W4GnguADyJ6NDolHklnyLXNgD9DsVgVtBe4vdB58TNSWekolJWP932RxVY8gePxlvY8cxFs7nnL3JAxEYOkw/zMABvF+JhN6ewoEhZLhmFIhZH1ep0k4n2KttlskpyMaj4lZbQi58Q79VlySLbbIOkMuYHKtvIH4Kh9xlTSttWGYcB6vT6qRvZ9j9fX19Q9YKVjhLNtMwsLHrkRJv7cfTtTkew+kXT/59LR/zattJ3fNi2kTLylWLkU0kY1L/ulpX11ATwWks7gZeNofzvqn4JRHhZGttttkozppZWOxRLKxyjIyMlU1Xaq+7GcXxFy7nOK2/FjpZsbQ5nrErAyeukom5WO6SX/Zj9dLuLZ9qEfp0nZ2J+Wk+3UoOZThZVT4iodvS5PId1n+53m9rcXoe/v4i1FY2Hk7e0N2+0Wm83maGSJl46PWen4nJ1HR+lsO5LTcrxEuRnkbPtdMmpGHeK34eGlmxsDecn+vLVtNS9brkLJlNAK+P7+nkaU2FSSUu12u6MKJ9t1FM0yN2XISpJbvsF+nlNTetRdcFseXrpzzKWQ/kLNzfb2nd62u8CKxzad7YNjpOPrrLj2WJQdQBo94s/1XH9b7vPmZJpbM0XixfL00hHfnuEFywvfz9q2hQ0bsV5fX7HZbFIB5f39Hev1+mjoFo851wZjF0BVVemxuUh9qstAsjwmTyWd/5afSyWtdLYLwG5sv/nJpRwfacv+9jU+BaQ0VVWl+3YqDv+2qaQv8syliOIxeSrpcsz1txGKxVTQz9q2lUfOBLCVSnZ22+IH5eLyCZyGA/yJan6+nY2wFJxtP0rL7ZR0EvL+eUrp5jq6fX8XgKPlE/xMbzu5lNKxvWZHpeRk8yP8fbTyXQR+iFlZlklkn2Iq1XxsnkK6U2mln0ya6+zO9Z/Z0j9Fs+lkLq200vm2HEVhOw7Ah345KzOPZWcXnBsOJvEeg6eQLocVzq8vaSOeXTKBA5J5n31x7ALgal22i8GPHgFwlAJSBE4e5XolbE8yhWRkZXSkuJSUqaqdhDrXJSDum6eSbq4DOBfpbPspV/63U3Nsxzbx8tniSC4dpCxN06S/KSwjII/JGd7DMHxYIPaSdFLi3TdPJR3JyWZHetiKJSMduwDe3t7S/dfX1zTqhNVKIL9UuW231XWdohoF41qVXdelNSx5XpSbkcxKzPfkgkO+XScej6eUDjhfTMmtY5JbToGjThjpfMTJ/W1XY6ZIbdumhWK5LJ5NS8uyTJXUxWKR2ow+0uVWbhaPxdNKB+QLKX44lxWL4yfZruNs77e3tyQdow3TRd/WomB1XSfR6rrGr1+/sFgs0qrMVrq6rrHb7VKl0lYt1+t1qoT6ZdM9EvAxeCrp5gb5XhLt7O8K5DZGv1zF0EY2rsbFqEbpuEQet6Zp0nsuFoskH7dcwWQOFVIei6eSLoeXLVfV9OL5MZZ28ysq+3TSSmcjnZWNf/N1jHr+R0DmugWUVj42TyWdHz6Vww8F4/5MFdu2TX1j/PGOw+GApmlS5LIpZNd1R5LxMV804X1fALF9hba/L9fuZJHGD1ez588vFXG/PIV031E+z6WJvMApmi2O2BSyaZq07LkVsmmaD6/zkcp+Cfguj9x8Pjuixi+rp+j3GDyFdJZzlT3/vB/XyDRwHEe0bZuiIS94RjumjqvVKqWNTB0pG2X1vxeXewzIz+z2k1f9Nve8uF8eXrqcWHNzz+bue/kY4fjLOLb4Ykv/9rfh2rb9IFuuX43dA7bNZs85N07ULgHoxbSfWbI9Bg8vXY7PFhusdCxqAEDf90kcCkDJeLtardC2bZLRypYrhtgxmjnxvHR+2s9cUchHO3G/PKV0OXLdCLZ9ZCMeCxJso3F/Kx0lWy6X6SesbBsuV3W0USv3xeDTRj/EzLfp5oa9ifvmqaTLpV2X7E98FwAHJ9vj+o5vCuelmzsvvx6K38+31ax0/rm5dFPi3TdPJZ1l7lt/rmroU0z+zBT72chqtTrqPnh5eUnSMTLytVYSO33nXDstl2YC+Ugn0R6Ph5fOX2xetnMj8/24yWmaUnrI9h2jXVEUqUrJLgN7y0jHIVq+7cVjnPosc5v/rHP3xf3z8NIB538gw49XpFg2FaQYnPU9jmMa8c+UsCiKVDhh94Bt0zHSUd7cT1eVZXmUYp5qn83dn4vgGq3yGDyFdEB+io0t1dvVt1hh5AXsV1CmKKxaWukY2ewYST8Q2bcDc5LkIpn9LL7AkrvvP/vc3+K+eArpfIc3ReIFTdnsyHwWSQAcpZDAn9K8nWnO17OQYkep5Mr/9kvAViwvSR0tSiOfj6eQLoef32bbatM0pb40AB8WEbKyWulspLMd6X5WAHB60Vc+f40iiKLc/fO00gEf2zm5pQ84+sRK4AceW+mYWto2oU0tKe+lbatTEe4SKTWh9fF4aumIb7OxmMHZA77vy1ccrQBMLymuTTWtdMBxtPVy2M22I3OL3lJi340w12enNPS+eXrpcl0CwJ8KJu/7/rG50j0Fs8UZX0TJkSuWzO1zqvBy6pjiMXhq6Vh9zLW1GOFIrrM6V+yo6/qDyL6YwqUW+BrNbxOWh5fu1PQdpmW+SOIrjNw31x/mq4d+fZKiKI7adkKc4+GlO4WVywvlO8tP3dr7uT4xiuff179WCODJpbP4iOeLG+QrgqhqKD7D00vnU00+NidKTsQ5rKCn+uckpbA8vXQeL4d//Dvfg/clnbD8GOnOtbNsJDyHb+udm70g8YRF5ba/4NyIEQkncvxY6SSCuBU/Jr28BFsE+Y4yv8QWOSRdhkuLLHZG+SXHEQL4wenl35CbN5fjOwYf+8HT9nE/aFqSPwY/OtJdK/3zQ8r88udfORbHfAJ/ph7ZoWfTNB0tCSHulx8r3SO2t/525Iy4D/SVKEQwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIhhJJ0Qwkk6IYCSdEMFIOiGCkXRCBCPphAhG0gkRjKQTIpj61ifwSEzT9OnXFEWRNv6dO5Z9fO59+Nyl52Hfd+54RVHMnou4Dop0V8CKURQFyrJMt1VVoSgKjOOIYRgwjiOqqkJVVQCAcRzR9z2maUJZlkfHHMcR4zh+OL6XhDL5be5cRSyKdN9A7oLOXfR+K8syicitrmssFgs0TYNhGNC2LYZhQF3XaJoGTdNgsVhgsVigruuj11q5L5VOxCPproBPJf1jVjRKNgwDuq5D3/dYLpcYhgFlWWKz2aRo2HUdlssllssluq5D27ZJwrquk4SUeU52cVsk3ZWw0Yzpno1slG2aJrRtCwBYrVYpjayqCm3bppSSf7dti3/++QfL5RIvLy/oui4JaOWzYp+SUMQj6T7BXBEkt18ulWQ7zUa5cRzRNA2maULXdRjHEYfDAQBQ13Vq95VliaZp0LbtkWxd152MdjnhxG2RdFcgJxwLKACOCiRVVWGaphT1KNBqtUqpJqVjW+/3799o2xa/f/9G0zR4eXlJUbBt2yPx5qKduB2S7htg+ujx4gF/hGMaCQBN0wBASjOZTrKKSemYki6XS7Rti67rUlHFRzpKPldc8efP8xXXR9JdAV7klAX4L1Vkemm7CVgwaZomibZcLtH3PQ6HA379+pXkpHSr1SrJx+hH6dq2/RDVfKTjOXrmvjzE9yLpvsCptp19zkc57m8vfishgCTP4XBI7T1Kx3YgI1zXdUdFGd73US1XRBG3Q9JdCSsbpbKRjpGH99kXNwxD2thRzg5xttHatkVVVWia5kM/32KxOGoz+j7BU5FOxCDprsipSDdNE6qqOhKSVUxKBgCHwyE9xmMxqvniDNt9HPFin8sNRRO3QdJdEZ/K+bSU0YsSst1HrICUqCiKlEJamW37LXcOEu5+kHRXJtf+8+nfqQHHp9qNdp85qXLSi9si6a7EqQvci3BKlHOc6q7IHSsndE58cT0kXQC3qBhe8n5WNkXBOIoz33D6+hPi62S/yTSfTohgJJ0QwUg6IYKRdEIEI+mECEbSCRGMpBMiGEknRDCSTohgJJ0QwUg6IYKRdEIEI+mECEbSCRGMpBMiGEknRDCSTohgJJ0QwUg6IYKRdEIEI+mECEbSCRHMuXUvtRiiEN+MIp0QwUg6IYKRdEIEI+mECEbSCRGMpBMimP8B8QpgqSpFbqIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lena = mpimg.imread('data/train/1.jpg') # 读取和代码处于同一目录下的 lena.png\n",
    "# 此时 lena 就已经是一个 np.array 了，可以对它进行任意处理\n",
    " #(126, 120, 3)\n",
    "print(lena.shape)\n",
    "theShape = lena.shape\n",
    "print(type(lena))\n",
    "# keepdims=True 保持维度\n",
    "lena2 = np.sum(lena,axis=2,keepdims=True)/3\n",
    "# print(np.sum(lena,axis=2,keepdims=True)/3)\n",
    "# print(lena2)\n",
    "\n",
    "\n",
    "\n",
    "for i in range(theShape[0]):\n",
    "    for j in range(theShape[1]):\n",
    "#         print(lena2[i,j,0])\n",
    "        if lena2[i,j,0]>200:\n",
    "            lena2[i,j,0]=0\n",
    "        else:\n",
    "            lena2[i,j,0] = 1\n",
    "            print(lena2[i,j,0])\n",
    "        \n",
    "\n",
    "# for i in lena2:\n",
    "#     for j in i:\n",
    "#         print(j)\n",
    "\n",
    "plt.imshow(lena) # 显示图片\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "coral-equity",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.]\n",
      "  [0.]\n",
      "  [0.]\n",
      "  ...\n",
      "  [0.]\n",
      "  [0.]\n",
      "  [0.]]\n",
      "\n",
      " [[0.]\n",
      "  [0.]\n",
      "  [0.]\n",
      "  ...\n",
      "  [0.]\n",
      "  [0.]\n",
      "  [0.]]\n",
      "\n",
      " [[0.]\n",
      "  [0.]\n",
      "  [0.]\n",
      "  ...\n",
      "  [0.]\n",
      "  [0.]\n",
      "  [0.]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.]\n",
      "  [0.]\n",
      "  [0.]\n",
      "  ...\n",
      "  [0.]\n",
      "  [0.]\n",
      "  [0.]]\n",
      "\n",
      " [[0.]\n",
      "  [0.]\n",
      "  [0.]\n",
      "  ...\n",
      "  [0.]\n",
      "  [0.]\n",
      "  [0.]]\n",
      "\n",
      " [[0.]\n",
      "  [0.]\n",
      "  [0.]\n",
      "  ...\n",
      "  [0.]\n",
      "  [0.]\n",
      "  [0.]]]\n"
     ]
    }
   ],
   "source": [
    "print(lena2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "heavy-commons",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  8. 11. 13. 15. 15. 17. 19. 21. 22. 22. 23. 25. 27.\n",
      " 28. 29. 30. 30. 30. 29. 25. 22. 21. 19. 15. 12. 12. 12. 12. 12. 12. 12.\n",
      " 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12.\n",
      " 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12.\n",
      " 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 10.  8.\n",
      "  6.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  7.  9. 10.\n",
      " 11. 12. 13. 13. 13. 14. 14. 13. 14. 14. 14. 14. 16. 17. 17. 82. 84. 85.\n",
      " 86. 86. 86. 86. 86. 86. 84. 82. 80.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "126\n",
      "120\n"
     ]
    }
   ],
   "source": [
    "tempRow = np.zeros(theShape[0])\n",
    "tempCollom= np.zeros(theShape[1])\n",
    "for i in range(theShape[0]):\n",
    "    for j in range(theShape[1]):\n",
    "        if lena2[i,j,0]==1:\n",
    "            tempRow[i] = tempRow[i]+1\n",
    "            tempCollom[j] = tempCollom[j]+1\n",
    "print(tempRow)\n",
    "print(tempCollom)\n",
    "print(len(tempRow))\n",
    "print(len(tempCollom))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "seventh-stewart",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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en+kFvAz8+ax55BUpyfXAfwHPAj/vmj9Bb+64qXE5TV9uo71x+S16b1KuoXeR/EBV/U2XAfcD64Cngdur6idLOlcLAS5JOlkLUyiSpHkY4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalR/w/AXyJDqo1HNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(tempRow, edgecolor='k', alpha=0.35) # 设置直方边线颜色为黑色，不透明度为 0.35   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "convenient-contribution",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 判断角点   行[23:108]   列[33:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "frank-quest",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
